/***************************************************************************************************
 * Copyright (c) 2017-2020, NVIDIA CORPORATION.  All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 *modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright notice,
 *this list of conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright
 *notice, this list of conditions and the following disclaimer in the
 *documentation and/or other materials provided with the distribution.
 *     * Neither the name of the NVIDIA CORPORATION nor the names of its
 *contributors may be used to endorse or promote products derived from this
 *software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 *AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 *IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 *DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY DIRECT,
 *INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 *DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
 *OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TOR (INCLUDING
 *NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
 *EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/
#pragma once

#include <stdexcept>
#include "cutlass/cutlass.h"
#include "cutlass/util/reference/device/kernel/tensor_foreach.h"

namespace cutlass {
namespace reference {
namespace device {

///////////////////////////////////////////////////////////////////////////////////////////////////

/// Launches a kernel calling a functor for each element in a tensor's index
/// space.
template <typename Func, int Rank, typename Params>
struct TensorForEach {
    /// Constructor performs the operation.
    TensorForEach(Coord<Rank> size, Params params = Params(), int grid_size = 0,
                  int block_size = 0) {
        if (!grid_size || !block_size) {
            // if grid_size or block_size are zero, query occupancy using the
            // CUDA Occupancy API
            cudaError_t result = cudaOccupancyMaxPotentialBlockSize(
                    &grid_size, &block_size,
                    reinterpret_cast<void const*>(
                            kernel::TensorForEach<Func, Rank, Params>));

            if (result != cudaSuccess) {
                throw std::runtime_error("Failed to query occupancy.");
            }

            // Limit block size. This has the effect of increasing the number of
            // items processed by a single thread and reduces the impact of
            // initialization overhead.
            block_size = (block_size < 128 ? block_size : 128);
        }

        dim3 grid(grid_size, 1, 1);
        dim3 block(block_size, 1, 1);

        kernel::TensorForEach<Func, Rank, Params>
                <<<grid, block>>>(size, params);
    }
};

///////////////////////////////////////////////////////////////////////////////////////////////////

/// Launches a kernel calling a functor for each element along a tensor's
/// diagonal
template <typename Func, int Rank, typename Params>
struct TensorDiagonalForEach {
    /// Constructor performs the operation
    TensorDiagonalForEach(Coord<Rank> size, Params params = Params(),
                          int start = 0, int end = -1, int block_size = 128) {
        if (end < 0) {
            end = size.min();
        }

        dim3 block(block_size, 1, 1);
        dim3 grid((end - start + block_size - 1) / block_size, 1, 1);

        kernel::TensorDiagonalForEach<Func, Rank, Params>
                <<<grid, block>>>(size, params, start, end);
    }
};

///////////////////////////////////////////////////////////////////////////////////////////////////

template <typename Element, typename Func>
struct BlockForEach {
    /// Constructor performs the operation.
    BlockForEach(Element* ptr, size_t capacity,
                 typename Func::Params params = typename Func::Params(),
                 int grid_size = 0, int block_size = 0) {
        if (!grid_size || !block_size) {
            // if grid_size or block_size are zero, query occupancy using the
            // CUDA Occupancy API
            cudaError_t result = cudaOccupancyMaxPotentialBlockSize(
                    &grid_size, &block_size,
                    reinterpret_cast<void const*>(
                            kernel::BlockForEach<Element, Func>));

            if (result != cudaSuccess) {
                throw std::runtime_error("Failed to query occupancy.");
            }

            // Limit block size. This has the effect of increasing the number of
            // items processed by a single thread and reduces the impact of
            // initialization overhead.
            block_size = (block_size < 128 ? block_size : 128);
        }

        dim3 grid(grid_size, 1, 1);
        dim3 block(block_size, 1, 1);

        kernel::BlockForEach<Element, Func>
                <<<grid, block>>>(ptr, capacity, params);
    }
};

///////////////////////////////////////////////////////////////////////////////////////////////////

}  // namespace device
}  // namespace reference
}  // namespace cutlass
